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  1. Transactions on Interactive Intelligent Systems (TiiS)
  2. ACM Transactions on Interactive Intelligent Systems (TiiS) : Volume 5
  3. Issue 2(Special Issue on Behavior Understanding for Arts and Entertainment (Part 1 of 2)), July 2015
  4. In the Mood for Vlog: Multimodal Inference in Conversational Social Video
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ACM Transactions on Interactive Intelligent Systems (TiiS) : Volume 7
ACM Transactions on Interactive Intelligent Systems (TiiS) : Volume 6
ACM Transactions on Interactive Intelligent Systems (TiiS) : Volume 5
Issue 4(Regular Articles and Special issue on New Directions in Eye Gaze for Interactive Intelligent Systems (Part 1 of 2)), January 2016
Issue 3(Special Issue on Behavior Understanding for Arts and Entertainment (Part 2 of 2) and Regular Articles), October 2015
Issue 2(Special Issue on Behavior Understanding for Arts and Entertainment (Part 1 of 2)), July 2015
Brief Introduction to the Special Issue on Behavior Understanding for Arts and Entertainment
The VideoMob Interactive Art Installation Connecting Strangers through Inclusive Digital Crowds
Affective Analysis of Professional and Amateur Abstract Paintings Using Statistical Analysis and Art Theory
In the Mood for Vlog: Multimodal Inference in Conversational Social Video
A General-Purpose Sensing Floor Architecture for Human-Environment Interaction
Context-Aware Automated Analysis and Annotation of Social Human--Agent Interactions
Issue 1, March 2015
ACM Transactions on Interactive Intelligent Systems (TiiS) : Volume 4
ACM Transactions on Interactive Intelligent Systems (TiiS) : Volume 3
ACM Transactions on Interactive Intelligent Systems (TiiS) : Volume 2
ACM Transactions on Interactive Intelligent Systems (TiiS) : Volume 1

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In the Mood for Vlog: Multimodal Inference in Conversational Social Video

Content Provider ACM Digital Library
Author Gatica-perez, Daniel Otsuka, Kazuhiro Sanchez-cortes, Dairazalia Kumano, Shiro
Copyright Year 2015
Abstract The prevalent “share what's on your mind” paradigm of social media can be examined from the perspective of mood: short-term affective states revealed by the shared data. This view takes on new relevance given the emergence of conversational social video as a popular genre among viewers looking for entertainment and among video contributors as a channel for debate, expertise sharing, and artistic expression. From the perspective of human behavior understanding, in conversational social video both verbal and nonverbal information is conveyed by speakers and decoded by viewers. We present a systematic study of classification and ranking of mood impressions in social video, using vlogs from YouTube. Our approach considers eleven natural mood categories labeled through crowdsourcing by external observers on a diverse set of conversational vlogs. We extract a comprehensive number of nonverbal and verbal behavioral cues from the audio and video channels to characterize the mood of vloggers. Then we implement and validate vlog classification and vlog ranking tasks using supervised learning methods. Following a reliability and correlation analysis of the mood impression data, our study demonstrates that, while the problem is challenging, several mood categories can be inferred with promising performance. Furthermore, multimodal features perform consistently better than single-channel features. Finally, we show that addressing mood as a ranking problem is a promising practical direction for several of the mood categories studied.
Starting Page 1
Ending Page 24
Page Count 24
File Format PDF
ISSN 21606455
e-ISSN 21606463
DOI 10.1145/2641577
Volume Number 5
Issue Number 2
Journal ACM Transactions on Interactive Intelligent Systems (TiiS)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-06-30
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Social video Mood Nonverbal behavior Verbal content Video blogs Vlogs
Content Type Text
Resource Type Article
Subject Artificial Intelligence Human-Computer Interaction
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